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Comparing spectral models to predict manganese content in Rosa spp. leaves using VIS-NIR data
Comparación de modelos espectrales para predecir el contenido de manganeso en hojas de Rosa spp. usando datos VIS-NIR
DOI:
https://doi.org/10.15446/agron.colomb.v43n1.118322Keywords:
PLSR, PCR, crop nutrition, spectral reflectance, spectral smoothing, predictive models, multivariate analysis, spectroradiometer (en)RMCP, RCP, nutrición de cultivos, reflectancia espectral, suavizado espectral, modelos predictivos, análisis multivariado, espectroradiómetro (es)
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This study, conducted on the Freedom rose cultivar grown under greenhouse conditions in the municipality of Tocancipá, Cundinamarca (Colombia), implemented Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) methods using visible and near infrared (VIS-NIR) spectroradiometry from 350 to 2500 nm to predict manganese (Mn) content in rose leaves. A randomized complete block design (RCBD) with manganese doses of 0%, 25%, 50%, 75%, and 100% of the reference dose of 2 mg L-1 was established in 25 plots with five treatments and five replicates. Samplings were conducted in the five phenological stages of “palmiche”, “rice”, “chickpea”, “scratch color”, and “straight sepals”, analyzing 10 plants per treatment, and spectral responses were measured on the adaxial leaf surface using the FieldSpec® 4 spectroradiometer. For model generation (PLSR and PCR), 24 predictive models were evaluated, comprising three spectral response ranges: range 1 (350-1000 nm), range 2 (350-1800 nm), and range 3 (350-2500 nm), applying different spectral correction methods: raw data (RD), Savitzky-Golay (SG), range normalization (RN), and Savitzky-Golay followed by range normalization (SG-RN). A total of 100 samples were used: 80 for calibration and 20 for external validation, randomly selected to represent the variability of the treatments. The spectral corrections improved the accuracy and robustness of the predictions, with the RN-PLSR and SG-RN-PLSR models showing the best performance metrics (R², RMSE, and RPD). The most relevant wavelengths were 523 nm, 557 nm, and around 720 nm, with correlations greater than 0.6 with the Mn concentration in leaves.
Este estudio, realizado en cultivo de rosa variedad Freedom sembrada bajo invernadero en el municipio de Tocancipá, Cundinamarca (Colombia), implementó métodos de Regresión por Mínimos Cuadrados Parciales (RMCP) y Regresión con Componentes Principales (RCP) utilizando espectroradiometría visible e infrarroja cercana (VIS-NIR) de 350 a 2500 nm para predecir el contenido de manganeso (Mn) en hojas de rosa. Se estableció un diseño de bloques completos al azar (BCA) con dosis de manganeso de 0%, 25%, 50%, 75% y 100% (referencia de 2 mg L-1) en 25 parcelas con cinco tratamientos y cinco repeticiones. Se realizaron muestreos en los cinco estados fenológicos “palmiche”, “arroz”, “garbanzo”, “rayando color” y “sépalos rectos”, analizando 10 plantas por tratamiento; las respuestas espectrales se midieron en la superficie adaxial de
las hojas utilizando el espectroradiómetro FieldSpec® 4. Para la generación de los modelos (RMCP y RCP), se evaluaron 24 modelos predictivos conformados por tres rangos de respuesta espectral: rango 1 (350 1000 nm), rango 2 (350-1800 nm) y rango 3 (350-2500 nm), aplicando diferentes métodos de corrección de espectro: datos crudos (RD), Savitzky Golay (SG), normalización por rangos (NR) y Savitzky-Golay seguido de normalización por rangos (SG-NR); se usaron 100 muestras en total: 80 para calibración y 20 para validación externa, seleccionadas aleatoriamente para representar la variabilidad de los tratamientos. Las correcciones del espectro mejoraron la precisión y solidez de la predicción, siendo los modelos NR-PLSR y SG-NR-PLSR los que presentaron las mejores valoraciones en las métricas (R², RMSE y RDP), mientras que las longitudes de onda más relevantes fueron 523 nm, 557 nm y cerca de 720 nm, con correlaciones superiores a 0,6 con la concentración de Mn en hojas.
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